Modelling hybrid and backpropagation adaptive neuro-fuzzy inference systems for flood forecasting

Ruhhee Tabbussum () and Abdul Qayoom Dar
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Ruhhee Tabbussum: National Institute of Technology Srinagar
Abdul Qayoom Dar: National Institute of Technology Srinagar

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 108, issue 1, No 22, 519-566

Abstract: Abstract The ability of the adaptive neuro-fuzzy inference algorithm architecture to simulate floods is explored in this research. The development of models for flood forecasting has been centered on two adaptive neuro-fuzzy inference (ANFIS) algorithms. The Takagi–Sugeno fuzzy inference systems (FIS) generated through subtracted clustering were trained using hybrid and backpropagation training algorithms. Multiple statistical performance evaluators were used to assess the performability of the established models. The validity and predictive power of the models are evaluated by estimating a flood occurrence in the study area. In designing the models, a total of 12 inputs were employed. The best performability was found for the ANFIS model created utilizing a hybrid training algorithm with mean square error (MSE) of 0.00034, co-efficient of correlation (R2) of 97.066%, root mean square error (RMSE) of 0.018, Nash–Sutcliffe model efficiency (NSE) of 0.968, mean absolute error (MAE) of 0.0073 and combined accuracy (CA) of 0.018, indicating the possible usage of exploiting the established model for prediction of floods.

Keywords: Adaptive neuro-fuzzy inference system; Backpropagation learning algorithm; Gaussian membership function; Hybrid learning algorithm; Subtractive clustering; Takagi–Sugeno fuzzy inference system (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11069-021-04694-w

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